Apex Technologies

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 The public face of artificial intelligence is the launch — a bigger model, a slicker demo, a new headline. The harder, quieter problem begins the moment those systems go live and have to run continuously, at scale, without failing in ways that cost real money. https://hackmd.io/@alexaa34/BJv0LzsEfe https://medium.com/@alexharris59600/behind-every-ai-launch-is-a-quieter-race-keeping-the-systems-from-failing-dad0b7e733c3 That problem is no longer abstract. On October 20, 2025, a single fault in one Amazon Web Services region cascaded across the internet, knocking thousands of companies in more than 60 countries offline for the better part of a day — from banking and trading apps to Snapchat and popular games. Weeks later, in November, a configuration error at Cloudflare briefly took down X, ChatGPT and Uber. Neither was a cyberattack; both began as small internal changes that rippled outward — a reminder of how much of modern life now rests on a handful of cloud systems, and on the ...

 Who is a data scientist? What does he do? What steps are involved in executing an end-to-end data science project? What roles are available in the industry? Will I need to be a good coder to be a good data scientist? How do we solve a data science problem in healthcare without domain knowledge of healthcare? Hold your horses. We will answer all the questions this week. Data scientist Creating a data science solution involves the following eleven steps: 1) Define the business problem, 2) Convert the business problem into an analytics problem, 3) Identify tables and columns relevant to the problem at hand, 4) Collect data, 5) Prepare data, 6) Explore data and derive insights, 7) Train model, 8) Evaluate model, 9) Deploy model, 10) Monitor and retrain model, and 11) Retire model. In a true sense, a data scientist should be able to do all the above. However, you may wonder whether a single individual could be an expert in every step of the process. You are right. That’s why we conside...

 As Western models inherit Western philosophy, India has a chance to bring its own intellectual heritage into the global AI conversation. If AI is going to influence how we think, shouldn’t we influence how AI thinks? Artificial Intelligence is no longer just a technological breakthrough. It is becoming a cultural force — one that shapes how we learn, work, communicate, and even make moral decisions. As AI systems grow more capable, the question is shifting from “How do we build smarter models?” to “What values and worldviews should guide these models?” A recent article from the MIT Initiative on the Digital Economy makes a striking observation: philosophy may be the next major frontier in LLM training. If AI systems are going to reason, interpret, and guide human behaviour, then the philosophical frameworks behind them matter as much as the data and algorithms. Today, most large language models (LLMs) are trained on datasets dominated by Western texts, Western ethics, and Western ...

 Who is a data scientist? What does he do? What steps are involved in executing an end-to-end data science project? What roles are available in the industry? Will I need to be a good coder to be a good data scientist? How do we solve a data science problem in healthcare without domain knowledge of healthcare? Hold your horses. We will answer all the questions this week. https://hackmd.io/@alexaa34/S1Ki10rEzx https://medium.com/@alexharris59600/data-science-roles-in-the-industry-0f442cc2f081 Data scientist Creating a data science solution involves the following eleven steps: 1) Define the business problem, 2) Convert the business problem into an analytics problem, 3) Identify tables and columns relevant to the problem at hand, 4) Collect data, 5) Prepare data, 6) Explore data and derive insights, 7) Train model, 8) Evaluate model, 9) Deploy model, 10) Monitor and retrain model, and 11) Retire model. In a true sense, a data scientist should be able to do all the above. However, you m...

 Expansion and the hidden risk Technological universities across India are expanding at unprecedented speed. New programmes in artificial intelligence, semiconductor technologies, clean-energy systems, robotics, biomedical devices and advanced manufacturing are being launched with urgency. Industry partnerships are deepening. Start-up ecosystems are growing. Applied research and commercialisation are celebrated as markers of relevance. This momentum reflects national ambition and global technological competition. Yet beneath this visible progress lies a quieter institutional risk that deserves serious attention. https://hackmd.io/@alexaa34/SkaZWF4EGl https://medium.com/@alexharris59600/foundations-before-frontiers-why-engineering-universities-must-safeguard-their-science-core-76e6a8b6a423 Can engineering remain transformative if the scientific foundations beneath it slowly weaken? Engineering is not simply about building systems. It is about understanding why systems work, when the...